Item Selection in Factor Analysis by Boosting and Specification Search R package OptimalFactor version 1.5.0 Provides an iterative optimization algorithm for exploratory and confirmatory factor analysis that follows a boosting approach inspired by machine learning. It uses a composite fit index whose weights adapt to the degrees of freedom and the sample size, following Kenny and McCoach (2003) <doi:10.1207/S15328007SEM1003_1> and Shi, Lee and Maydeu-Olivares (2019) <doi:10.1177/0013164418783530>. It detects and removes problematic items (Heywood cases, cross-loadings, low loadings) with greedy or global search strategies, checks interfactor correlations, and supports ordinal data with WLSMV estimation and several rotation methods. A heuristic specification search for confirmatory models (MacCallum, 1986 <doi:10.1037/0033-2909.100.1.107>) explores move, drop and covariance operations across seed configurations. It also evaluates the resampling stability of the item selection, the recovery of a known or theoretical structure, and offers an optional conceptual analysis of removed items through a large language model service. Software Depends: R (>= 4.0.0) Imports: lavaan, psych, dplyr, tidyr, ggplot2, stringr, parallel, stats, utils Suggests: testthat (>= 3.0.0), httr, jsonlite, R.utils, knitr, rmarkdown, semTools Jose Ventura-Leon <jventuraleon@gmail.com> Comprehensive R Archive Network (CRAN) Jose Ventura-Leon [aut, cre] (ORCID: <https://orcid.org/0000-0003-2996-4244>) GPL (>= 3) 2026-10-24 application/tgz https://CRAN.R-project.org/package=OptimalFactor doi:10.32614/CRAN.package.OptimalFactor